Dynamic Neighborhood Reduction for Point Cloud Entropy Coding
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Solution Overview
Problem
Existing methods for encoding and decoding point cloud geometry data struggle with efficiently utilizing large causal neighborhoods, leading to poor compression capabilities due to the high number of possible neighborhood configurations and inadequate statistical building for entropy coders.
Innovation Solution
The proposed method involves a dynamic reduction function that progressively reduces neighborhood configurations based on usage statistics, updating the tree structure and entropy coder selection dynamically to optimize encoding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a large causal neighborhood is used for encoding point cloud geometry data, then the statistical information available for entropy coders increases, but the number of possible neighborhood configurations becomes excessively large, leading to memory and computational issues
Solution Approach 1:
The patent segments the large set of neighborhood configurations into multiple smaller groups or categories. Instead of treating all possible neighborhood configurations as a single large set, the method divides them into manageable segments that can be processed separately, reducing the memory burden while preserving the statistical information from the full neighborhood.
Solution Approach 2:
The patent implements a dynamic approach where the neighborhood configuration grouping is not fixed but adapts based on the data being encoded. The system dynamically selects and updates the grouping of neighborhood configurations during the encoding process, allowing it to optimize for the specific statistical patterns present in different regions of the point cloud data.
2Productivity
If all possible neighborhood configurations are maintained for entropy coder selection, then the compression capability is maximized, but the memory footprint becomes excessively large
Solution Approach 1:
The patent extracts only the most relevant or frequently occurring neighborhood configurations from the complete set of possible configurations. By identifying and retaining only the essential configurations that contribute most to compression performance, the system achieves good compression capability with a significantly reduced memory footprint.
Solution Approach 2:
The patent changes the parameter of neighborhood configuration representation by using a reduced set of configuration indices or categories instead of maintaining all possible detailed configurations. This parameter transformation allows the system to reference neighborhood information efficiently using fewer bits while still capturing the essential statistical patterns needed for effective compression.
3Ease of manufacture
If a fixed neighborhood configuration set is used, then the implementation is simpler, but the adaptation to different data patterns is limited
Solution Approach 1:
The patent introduces dynamics into the neighborhood configuration selection process, allowing the system to adapt to different data patterns while maintaining implementation feasibility. The method dynamically updates or selects from multiple pre-defined grouping strategies based on the characteristics of the data being encoded, providing adaptability without requiring a completely fixed or completely flexible approach.
Solution Approach 2:
The patent performs preliminary organization of neighborhood configurations into grouped categories before the actual encoding process. By pre-processing and structuring the configuration sets in advance, the system simplifies the runtime implementation while still maintaining the ability to adapt to different data patterns through the selection and update of these pre-organized groups.
Data Source
AI summary
A method of encoding a series of data into a bitstream, includes: obtaining a current neighborhood configuration relative to a current data of the series of data, the set of candidate neighborhood configurations being obtained from previously encoded data of the series of data; obtaining a reduced current neighborhood configuration by applying a dynamic reduction function to the current neighborhood configuration, an image of the dynamic reduction function being a set of reduced neighborhood configurations and each reduced neighborhood configuration being associated with a leaf node of a tree, the reduced current neighborhood configuration being obtained by progressing from a root node of the tree until a leaf node based on values of constitutive elements of the current neighborhood configuration; and encoding the current data by using an entropy coder of a set of entropy coders, the entropy coder being selected based on the reduced current neighborhood configuration.